AI Deployment 2019: Preparation and Precautions

AI is slowly invading our lives, from our smartphone to other connected devices. AI implementation has not only impacted our business as well as our personal lives. It might possible you are using AI assistants and connected devices in your house, however, the business world hasn’t fully jumped on board yet.

According to a recent report, 68 per cent decision makers of Indian business believe that AI will help them to boost productivity and generate growth. But AI deployment is not easy, there are multiple hurdles to address. While businesses understand the advantages of implementing AI, yet they are not ready to modernize their decade-old infrastructure.

It is important to upgrade your business models to achieve new height, but to deploy AI you need to be ready to tackle AI challenges. Let’s have a look at common AI deployment challenges which you will come across.

According to Nishikant Nigam, EVP and Chief Delivery Officer, CSS Corp.

We live in very exciting times. The AI debate is intensifying. There is a constant commentary on it from some of the finest brains of our times. Some are for it with gusto and some asking for caution. One thing is certain that advances in AI have accelerated in recent times like no other technology has done in the past few decades. Artificial Intelligence is pushing the envelope on what machines are capable of doing in all facets of life. It is very clear that AI will become much more pervasive and intertwined in our day to day life. It will change the way we live in fundamental ways.

AI is the ultimate frontier in technology. Organizations are investing significantly in AI and it will continue to happen with AI becoming more of an edge than an enabler for businesses. However, we often see organizations struggle to effectively leverage and implement AI capabilities to drive customer experience, efficiency and productivity improvements. It is about finding the right business case, algorithms and reimagining operations with AI.

AI-driven algorithms are evolving towards a state where AI powered services offer a personalized experience when communicating with humans. These AI-driven algorithms have the potential to constantly learn from a customer’s interactions and build on their ever-growing knowledge base. For instance, today in the customer support industry, AI powered chatbots understand the customer’s intent and respond to their queries in real time. Incredibly, they remember facts, learn from previous conversations, and can access online information by integrating with enterprise systems. Their ability to troubleshoot, answer complex questions, and engage in interactive dialogue makes them a third-generation customer engagement solution.

There are various AI powered solutions in the market today to manage the customer support ecosystem. However, it would be premature to state that all these solutions are effectively solving customer problems. Different solutions are in different stages of evolution and maturity. So, it is extremely important to adopt a pragmatic and portfolio-based approach towards adoption and execution.

According to Suman Reddy Eadunuri, MD, Pegasystems India

AI transparency and explainability will permeate customer engagement: GDPR and CCPA reflect a societal demand for data privacy and decision transparency, and for many organizations, this means reexamining how they’re using AI. On the one hand, AI can be a black box and overflowing with learned biases; on the other, it can drive much smoother, more personalized customer engagement.

Organizations can control and regulate AI judgement/decisions and how it ties into the ability to manage bias and ethical issues. Based on increasing customer demands and regulations, the ability to fully explain AI decisioning will become a key factor in how businesses deploy this power in the year ahead.

According to Faisal Husain, Co-founder and CEO, Synechron

We at Synechron follow the two untold universal truths about AI, which serve as starting points for effectively building people’s understanding, engagement, and role in our AI journey.

AI needs to be diversified: AI must be developed by people who understand the business and domain problem (not solely the technology). The gravity of some applications (e.g., credit scoring or default prediction) demands diverse perspectives, including human and cultural diversity, multidisciplinary expertise, and workflows to monitor domain dynamics. It takes a team, not an individual.

AI is directional: AI is continuously learning from its past actions and never stops reinventing itself; it is full of probabilities and requires critical thinking on part of the programmers due to errors in judgments. This is where the right skill will play the most important role – by designing AI systems to balance the benefits, risks, and audit ability of the application.”